Abstract

Solving nonlinear equation systems (NESs) is an important yet challenging task in the field of numerical computation. It aims to locate multiple roots in a single run. However, the existing methods lack effective knowledge transfer. In this article, a knowledge transfer-based adaptive differential evolution is proposed to deal with NESs. Its main features are: (i) knowledge transfer between two niching techniques (crowding and speciation) is carried out to balance diversity and convergence; (ii) the variation characteristics of population diversity and convergence are used to judge knowledge transfer intensity; (iii) a knowledge transfer mechanism is designed to ensure that reasonable individuals are selected for the transfer to supplement the deficiencies of crowding and speciation; (iv) a parameter adaptation with niching level is introduced to improve search efficiency. Experiments on classical 30 NES problems have demonstrated that the proposed approach can outperform the state-of-the-art algorithms, in terms of root ratio and success rate.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call